Prediction of solar flares is critical for minimizing the impact of space weather on communication and power systems. This study explores the application of machine learning models—Long Short-Term Memory (LSTM), Random Forest (RF), and XGBoost (XGB)—to forecast solar flares using multivariate time series data derived from SHARP and GOES. The experimental setup systematically varies two key temporal parameters: window size (12, 24, and 48 hours) and prediction lag (12, 24, and 48 hours). To address the strong class imbalance in flare data, cost-sensitive learning is incorporated using a sample-based class weighting strategy. Results demonstrate that the LSTM model achieves the best performance when using a short 12-hour window and a 12-hour prediction lag, reaching 89% accuracy, a True Skill Statistic (TSS) of 0.6949, and an F1-score of 0.6873. These findings emphasize the importance of short-term temporal dependencies and class imbalance mitigation for improving solar flare forecasting performance.

The ornamental rock industry in Brazil is distinguished by its diverse assortment of rock types, presenting a unique challenge in classification due to its inherent subjectivity and reliance on expert judgment. to address this problem, the present study introduces a publicly accessible database encompassing 12 distinct classes of ornamental rocks, including granite, marble, and quartzite. This database comprises 1,798 images sourced directly from entities within the stone sector. We employ and compare the performance of seven neural network models for ornamental rock image classification: VGG16, VGG19, ResNet50, ResNet101, Xception, InceptionV3, and the Vision Transformer (ViT). Our empirical analysis reveals that the ViT model outperforms conventional architectures, achieving an accuracy rate of 98.36%.

The efficiency and accessibility of healthcare delivery systems can be enhanced through solutions that minimize the impact of patient No-Shows for medical exams and appointments. The significant disparity between the “Show” and “No-Show” categories within the dataset can impair the efficacy of predictive models, necessitating the employment of dataset balancing techniques before classifier training. This study evaluated various methods to address imbalances in datasets for predicting patient No-Shows. Techniques such as undersampling (Random Removal - RR, Remove Similar - RS, Remove Farthest - RF) and oversampling (Adaptive Synthetic Sampling - ADASYN) were applied to adjust the balance between Show and No-Show categories to ratios of 80–20%, 70–30%, 60–40%, and 50–50%, alongside a cost-sensitive learning approach. Four classifiers were deployed: Support Vector Machine (SVM), Naive Bayes, k-Nearest Neighbors (k-NN), and Random Forests. Additionally, a decision tree produced by the C4.5 algorithm was utilized for the cost-sensitive learning approach. The classifiers were evaluated using metrics such as Precision, Recall, F-measure, and AUC/ROC. Among the various methods tested, the RR combined with the k-NN classifier achieved the highest AUC/ROC value. However, due to the longer computational time of k-NN, the Random Forest classifier emerged as a more pragmatic choice when processing time is a critical consideration.

This paper presents a novel approach for forecasting gas consumption in pelletizing processes in the steel industry by integrating AutoML techniques based on neuroevolution. The pursuit of energy efficiency and the reduction of harmful gas emissions is a pressing challenge for industrial sustainability. In this context, we developed and implemented MLP and LSTM neural network models, optimized through neuroevolution strategies, to enhance the accuracy of gas consumption forecasts. The use of AutoML allowed for the automation of model selection and tuning, significantly reducing the need for manual intervention and improving the effectiveness of the predictive models. The results showed that the LSTM model, optimized by neuroevolution, significantly outperformed traditional methods, achieving an RMSE of 0.39, demonstrating not only the superior accuracy of the proposed approach but also its practical relevance for the industry. Additionally, the study highlighted the efficiency of neuroevolution in configuring adaptive network architectures that efficiently respond to the complex dynamics of industrial data. These findings support the adoption of AutoML as a strategic tool for optimizing industrial processes, contributing to the literature on practical applications of advanced machine learning and energy resource management in industrial environments.